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Extending Excel with Python and R

You're reading from   Extending Excel with Python and R Unlock the potential of analytics languages for advanced data manipulation and visualization

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Length 344 pages
Edition 1st Edition
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Authors (2):
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Steven Sanderson Steven Sanderson
Author Profile Icon Steven Sanderson
Steven Sanderson
David Kun David Kun
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David Kun
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Table of Contents (20) Chapters Close

Preface 1. Part 1:The Basics – Reading and Writing Excel Files from R and Python
2. Chapter 1: Reading Excel Spreadsheets FREE CHAPTER 3. Chapter 2: Writing Excel Spreadsheets 4. Chapter 3: Executing VBA Code from R and Python 5. Chapter 4: Automating Further – Task Scheduling and Email 6. Part 2: Making It Pretty – Formatting, Graphs, and More
7. Chapter 5: Formatting Your Excel Sheet 8. Chapter 6: Inserting ggplot2/matplotlib Graphs 9. Chapter 7: Pivot Tables and Summary Tables 10. Part 3: EDA, Statistical Analysis, and Time Series Analysis
11. Chapter 8: Exploratory Data Analysis with R and Python 12. Chapter 9: Statistical Analysis: Linear and Logistic Regression 13. Chapter 10: Time Series Analysis: Statistics, Plots, and Forecasting 14. Part 4: The Other Way Around – Calling R and Python from Excel
15. Chapter 11: Calling R/Python Locally from Excel Directly or via an API 16. Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study
17. Chapter 12: Data Analysis and Visualization with R and Python in Excel – A Case Study 18. Index 19. Other Books You May Enjoy

Other alternatives

Apart from pandas and openpyxl, there are other libraries available for exporting data to Excel from Python. Some popular alternatives include XlsxWriter, xlrd, and xlwt. These libraries offer different features and capabilities, and the choice depends on your specific requirements. XlsxWriter, for example, emphasizes performance and supports advanced Excel features, while xlrd and xlwt provide functionality for reading and writing older Excel file formats (.xls).

In this section, we explored the benefits of exporting data to Excel, demonstrated how to do so using pandas, went through the process of creating and manipulating Excel sheets using openpyxl, discussed the reasons for choosing openpyxl or pandas based on your needs, and mentioned other alternatives available. By leveraging the power of these libraries, you can seamlessly export your data from Python to Excel, enabling efficient analysis, reporting, and collaboration.

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